The spatial loss usually has two parts, content loss and style loss [1]}. The former is focused on preserving the content of the content image, and the latter is focused on transferring the style to the style image. Current learning-based neural networks for image style transfer usually rely on a neural network called the loss network the parameters of which are pretrained on other tasks (e.g., classification on the ImageNet dataset) and fixed. Both content loss and style loss are computed on the top of this loss network and are used to train a stylization network d parameters need to be learned.
